Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method

DOI10.1155/2012/431512zbMath1253.91138OpenAlexW2153667436WikidataQ58700553 ScholiaQ58700553MaRDI QIDQ1925502

Tao Xiong, Yukun Bao, Zhongyi Hu

Publication date: 18 December 2012

Published in: Discrete Dynamics in Nature and Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2012/431512

zbMATH Keywords

air passenger traffic forecastingimplicit seasonality

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Economic time series analysis (91B84) Learning and adaptive systems in artificial intelligence (68T05) Traffic problems in operations research (90B20) Statistical methods; economic indices and measures (91B82)

Related Items (1)

A study on the generalized approximation modeling method based on fitting sensitivity for prediction of engine performance

Uses Software

• forecast
• LIBSVM
• Forecast

Cites Work

• A confidence limit for the empirical mode decomposition and Hilbert spectral analysis
• One or Two Frequencies? The Empirical Mode Decomposition Answers
• An EMD-Based Neural Network Ensemble Learning Model for World Crude Oil Spot Price Forecasting

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